getMotifAnnotation: Get motif annotation

Description Usage Arguments Value See Also Examples

View source: R/02_addMotifAnnotation.R

Description

Get the genes/transcription factors annotated to the given motifs

Usage

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getMotifAnnotation(
  motifs,
  motifAnnot,
  annotCats = c("directAnnotation", "inferredBy_MotifSimilarity",
    "inferredBy_Orthology", "inferredBy_MotifSimilarity_n_Orthology"),
  idColumn = "motif",
  returnFormat = c("asCharacter", "subset", "list")[1],
  keepAnnotationCategory = TRUE
)

Arguments

motifs

Motif IDs

motifAnnot

Motif annotation database containing the annotations of the motif to genes or transcription factors.

annotCats

Annotation categories to be considered: "directAnnotation" (annotated in the source database), "inferredBy_Orthology" (the motif is annotated to an homologous/ortologous gene), or inferred based on motif similarity ("inferredBy_MotifSimilarity", "inferredBy_MotifSimilarity_n_Orthology").

idColumn

Annotation column containing the ID (e.g. motif, accession)

returnFormat

Determines the output format. Choose one of the following values:

keepAnnotationCategory

Include annotation type in the TF information?

  • asCharacter: Named vector with the genes or TFs annotated to the given motifs (in the same order, including empty and duplicated values).

  • subset: Subset of the annotation table (list split by motif)

  • list: List of TF names (unique values), duplicated motifs or motifs without annotation are not returned.

Value

See argument returnFormat

See Also

addMotifAnnotation add the annotation directly to the motif enrichment results.

See the package vignette for examples and more details: vignette("RcisTarget")

Examples

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##################################################
# Setup & previous steps in the workflow:

#### Gene sets
# As example, the package includes an Hypoxia gene set:
txtFile <- paste(file.path(system.file('examples', package='RcisTarget')),
                 "hypoxiaGeneSet.txt", sep="/")
geneLists <- list(hypoxia=read.table(txtFile, stringsAsFactors=FALSE)[,1])

#### Databases
## Motif rankings: Select according to organism and distance around TSS
## (See the vignette for URLs to download)
# motifRankings <- importRankings("hg19-500bp-upstream-7species.mc9nr.feather")

## For this example we will use a SUBSET of the ranking/motif databases:
library(RcisTarget.hg19.motifDBs.cisbpOnly.500bp)
data(hg19_500bpUpstream_motifRanking_cispbOnly)
motifRankings <- hg19_500bpUpstream_motifRanking_cispbOnly

## Motif - TF annotation:
data(motifAnnotations_hgnc) # human TFs (for motif collection 9)
motifAnnotation <- motifAnnotations_hgnc

### Run RcisTarget
# Step 1. Calculate AUC
motifs_AUC <- calcAUC(geneLists, motifRankings)

##################################################

### (This step: Step 2)
# Before starting: Setup the paralell computation
library(BiocParallel); register(MulticoreParam(workers = 2)) 
# Select significant motifs, add TF annotation & format as table
motifEnrichmentTable <- addMotifAnnotation(motifs_AUC,
                                           motifAnnot=motifAnnotation)

# Alternative: Modifying some options
motifEnrichment_wIndirect <- addMotifAnnotation(motifs_AUC, nesThreshold=2, 
        motifAnnot=motifAnnotation,
        highlightTFs = "HIF1A", 
        motifAnnot_highConfCat=c("directAnnotation"), 
        motifAnnot_lowConfCat=c("inferredBy_MotifSimilarity",
                                "inferredBy_MotifSimilarity_n_Orthology",
                                "inferredBy_Orthology"),
        digits=3)

# Getting TFs for a given TF:
motifs <- motifEnrichmentTable$motif[1:3]

getMotifAnnotation(motifs, motifAnnot=motifAnnotation)
getMotifAnnotation(motifs, motifAnnot=motifAnnotation, returnFormat="list")

### Exploring the output:
# Number of enriched motifs (Over the given NES threshold)
nrow(motifEnrichmentTable)

# Interactive exploration
motifEnrichmentTable <- (motifEnrichmentTable)
DT::datatable(motifEnrichmentTable, filter="top", escape=FALSE,
              options=list(pageLength=50))
# Note: If using the fake database, the results of this analysis are meaningless

# The object returned is a data.table (for faster computation),
# which has a diferent syntax from the standard data.frame or matrix
# Feel free to convert it to a data.frame (as.data.frame())
motifEnrichmentTable[,1:6]


##################################################
# Next step (step 3, optional):
## Not run: 
  motifEnrichmentTable_wGenes <- addSignificantGenes(motifEnrichmentTable,
                                                     geneSets=geneLists,
                                                     rankings=motifRankings,
                                                     method="aprox")

## End(Not run)

aertslab/RcisTarget documentation built on April 21, 2021, 9:46 a.m.